1887
Volume 16 Number 1
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Missing and irregular ground‐penetrating radar trace data resulting from sampling conditions are important issues in engineering. This study adopted compressive sensing theory to reconstruct missing ground‐penetrating radar trace data. A ground‐penetrating radar data reconstruction method was established based on compressive sensing theory and K‐singular value decomposition. The method used the sampling matrix of the missing data as the measurement matrix and the K‐singular value decomposition algorithm to obtain a complete dictionary of sparse coefficients. A traditional dictionary cannot be adaptively adjusted according to the data features; the proposed method resolved this problem. The iteratively reweighted least‐squares method was used to reconstruct the missing trace data. Two experiments on the recovery of missing ground‐penetrating radar data through a simulation and a field example were conducted to test the feasibility and effectiveness of the proposed method.

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/content/journals/10.3997/1873-0604.2017030
2017-05-01
2020-05-26
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References

  1. AdizuaO.F., EbeniroJ.O. and EhirimC.N.2015. Application of the frequency‐wave number (FK) and the radial trace transform (RTT) in the attenuation of coherent noise in onshore seismic data. Asian Journal of Earth Sciences8(1), 15.
    [Google Scholar]
  2. AndrésA.M., PadovaniS., TepperM. and Jacobo‐BerllesJ.2014. Face recognition on partially occluded images using compressed sensing. Pattern Recognition Letters36, 235–242.
    [Google Scholar]
  3. BaraniukR.G., CevherV., DuarteM.F. and HegdeC.2010. Model‐based compressive sensing. IEEE Transactions on Information Theory56(4), 1982–2001.
    [Google Scholar]
  4. ChartrandR. and YinW.2008. Iteratively reweighted algorithms for compressive sensing. IEEE International Conference on Acoustics, Speech and Signal Processing.
    [Google Scholar]
  5. DonohoD.L.2006. Compressed sensing. IEEE Transactions on Information Theory52(4), 1289–1306.
    [Google Scholar]
  6. FengX., SatoM. and LiuC.2011. Hand‐held GPR imaging using migration for irregular data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing4(4), 799–803.
    [Google Scholar]
  7. GanS., WangS., ChenY., ChenX., HuangW. and ChenH.2016. Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform. Journal of Applied Geophysics130, 194–208.
    [Google Scholar]
  8. GangopadhyayD., AllstotE.G., DixonA.M., NatarajanK., GuptaS. and AllstotD.J.2014. Compressed sensing analog front‐end for bio‐sensor applications. IEEE Journal of Solid‐State Circuits49(2), 426–438.
    [Google Scholar]
  9. GiannopoulosA.2005. Modelling ground penetrating radar by GprMax. Construction and Building Materials19(10), 755–762.
    [Google Scholar]
  10. GroenenboomJ. and YarovoyA.2002. Data processing and imaging in GPR system dedicated for landmine detection. Sensing and Imaging3(4), 387–402.
    [Google Scholar]
  11. GurbuzA.C., McClellanJ.H. and ScottW.R.2009. A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Transactions on Signal Processing57(7), 2640–2650.
    [Google Scholar]
  12. KimB., JeongS. and ByunJ.2015. Trace interpolation for irregularly sampled seismic data using curvelet‐transform‐based projection onto convex sets algorithm in the frequency–wavenumber domain. Journal of Applied Geophysics118, 1–14.
    [Google Scholar]
  13. LeleQ., YuqingY., YanpengS. and LiliZ.2015. Diffraction tomographic ground‐penetrating radar multibistatic imaging algorithm with compressive frequency measurements. IEEE Geoscience and Remote Sensing Letters12(10), 2011–2015.
    [Google Scholar]
  14. LingalaS.G. and JacobM.2013. Blind compressive sensing dynamic MRI. IEEE Transactions on Medical Imaging32(6), 1132–1145.
    [Google Scholar]
  15. MaJ.2013. Three‐dimensional irregular seismic data reconstruction via low‐rank matrix completion. Geophysics78(5), V181–V192.
    [Google Scholar]
  16. MetzlerC.A., MalekiA. and BaraniukR.G.2014. From denoising to compressed sensing. Eprint Arxiv.
    [Google Scholar]
  17. NaghizadehM.2012. Seismic data interpolation and denoising in the frequency‐wavenumber domain. Geophysics77(2), V71–V80.
    [Google Scholar]
  18. SuksmonoA.B., BharataE., LestariA.A., YarovoyA.G. and LigthartL.P.2010. Compressive stepped‐frequency continuous‐wave ground‐penetrating radar. IEEE Geoscience and Remote Sensing Letters7(4), 665–669.
    [Google Scholar]
  19. TongJ., LiuJ. and CaiQ.2013. The PET image reconstruction based on weighted least‐squares and TV penalty. Acta Electronica Sinica4, 26.
    [Google Scholar]
  20. TsaigY. and DonohoD.L.2006. Extensions of compressed sensing. Signal Processing86(3), 549–571.
    [Google Scholar]
  21. WangX., GengY., WuR.S. and SongP.2015. Seismic data reconstruction in Dreamlet domain based on compressive sensing. Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting50, 399–404.
    [Google Scholar]
  22. ZhangH., ChenX.‐H. and WuX.‐M.2013. Seismic data reconstruction based on CS and Fourier theory. Applied Geophysics10(2), 170–180.
    [Google Scholar]
  23. ZhouY., WangL. and PuQ.2014. Seismic data reconstruction based on K‐SVD dictionary learning under compressive sensing framework. Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting49(4), 652–660.
    [Google Scholar]
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